Stock Market Prediction Using Deep Learning Approach

Authors

  • Suresh Dara Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Pradesh, India Author
  • A Gayathri Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Pradesh, India Author
  • K Deepika Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Pradesh, India Author
  • L Anitha Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Pradesh, India Author
  • M Madhu Priya Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Pradesh, India Author
  • K Indu Department of Computer Science & Engineering, PACE Institute of Technology & Sciences, Ongole, Andhra Pradesh, India Author

DOI:

https://doi.org/10.55524/ijirem.2023.10.2.26

Keywords:

Stock Prediction, Peep Learning, CNN, RNN

Abstract

Stock market prediction is a challenging  task that has attracted a lot of attention from both academic  and industrial communities. In recent years, deep learning  has emerged as a powerful tool for stock prediction due to its  ability to handle large amounts of complex data. In this arti cle, we review the state-of-the-art deep learning techniques  for stock prediction and provide insights into their strengths  and limitations. Specifically, we focus on the application of  recurrent neural networks (RNNs) and convolutional neural  networks (CNNs) in stock prediction, and discuss the chal 

lenges and opportunities for future research in this area. 

Downloads

Download data is not yet available.

References

Sharma, Ashish, Dinesh Bhuriya, and Upendra Singh. "Survey of stock market prediction using machine learning approach." 2017 International conference of electronics, communication and aerospace technology (ICECA). Vol. 2. IEEE, 2017.

Lauzon, Fran cis Quintal. "An introduction to deep learning." 2012 11th international conference on information science, signal processing and their applications (ISSPA). IEEE, 2012.

Shah, Jaimin, Darsh Vaidya, and Manan Shah. "A comprehen sive review on multiple hybrid deep learning approaches for stock prediction." Intelligent Systems with Applications (2022): 200111.

Laptev, Nikolay, et al. "Time-series extreme event forecasting with neural networks at uber." International conference on ma chine learning. Vol. 34. sn, 2017.

Liu, Hui, Xiwei Mi, and Yanfei Li. "Smart deep learning based wind speed prediction model using wavelet packet decomposi tion, convolutional neural network and convolutional long short term memory network." Energy Conversion and Man agement 166 (2018): 120-131.

Li, Yang, and Yi Pan. "A novel ensemble deep learning model for stock prediction based on stock prices and news." Interna tional Journal of Data Science and Analytics (2022): 1-11.

Lu, Wenjie, et al. "A CNN-LSTM-based model to forecast stock prices." Complexity 2020 (2020): 1-10.

Downloads

Published

2023-04-30

Issue

Vol. 10 No. 2 (2023): International Journal of Innovative Research in Engineering & Management (Apr) 2023

Section

Articles

How to Cite

Stock Market Prediction Using Deep Learning Approach . (2023). International Journal of Innovative Research in Engineering & Management, 10(2), 130–132. https://doi.org/10.55524/ijirem.2023.10.2.26
Crossref
Scopus
Google Scholar
Europe PMC